Equip tourists with knowledge mined from travelogues

Abstract

With the prosperity of tourism and Web 2.0 technologies, more and more people have willingness to share their travel experiences on the Web (e.g., weblogs, forums, or Web 2.0 communities). These so-called travelogues contain rich information, particularly including location-representative knowledge such as attractions (e.g., Golden Gate Bridge), styles (e.g., beach, history), and activities (e.g., diving, surfing). The location-representative information in travelogues can greatly facilitate other tourists' trip planning, if it can be correctly extracted and summarized. However, since most travelogues are unstructured and contain much noise, it is difficult for common users to utilize such knowledge effectively. In this paper, to mine location-representative knowledge from a large collection of travelogues, we propose a probabilistic topic model, named as Location-Topic model. This model has the advantages of (1) differentiability between two kinds of topics, i.e., local topics which characterize locations and global topics which represent other common themes shared by various locations, and (2) representation of locations in the local topic space to encode both location-representative knowledge and similarities between locations. Some novel applications are developed based on the proposed model, including (1) destination recommendation for on flexible queries, (2) characteristic summarization for a given destination with representative tags and snippets, and (3) identification of informative parts of a travelogue and enriching such highlights with related images. Based on a large collection of travelogues, the proposed framework is evaluated using both objective and subjective evaluation methods and shows promising results.

SNS user classification and its application to obscure POI discovery

“…Unlike conventional studies that discover famous or popular POIs using a voting approach [2][3][4][5], because of the lack of related raw data on the Internet, our application discovers POIs that are not well known by analyzing information asymmetries among different user groups. This application well demonstrates the usefulness and novelty of the proposed models.…”

“…Although obscure locations are less well known, they are often still worth visiting, and they are a valuable sightseeing resource for development and promotion. Recently, several studies [2][3][4][5] have shown the potential of UGC mining for discovering popular or famous locations and landmarks. Nevertheless, this area of research is relatively unexplored.…”

An Overview of Flickr Challenges and Research Opportunities

“…Yanai et al [64], [65] tried to detect cultural differences regarding certain world widespread concepts. Hao et al [66] presented a methodology for the automatic creation of travelogues, i.e., a kind of a travel-related experience logging. They retrieved geo-tagged photos from Flickr and embed them in these travelogues.…”

Automatic Compilation of Travel Information from Texts: A Survey

“…Hao et al [10] proposed a method for mining location-representative knowledge from travel blogs based on a probabilistic topic model (the Location-Topic model). Using this model, Figure 4.…”

“…They adopted the method of clustering blog photos relating to a particular tourist site, such as Louvre Museum in Paris. 10 Then they represented these photos as a graph based on the clustering results, and detected landmarks using link analysis methods, such as the PageRank [3] and HITS [19] algorithms.…”